Remote sensing can provide crucial information for planetary rovers. However, they must validate these orbital observations with in situ measurements. Typically, this involves validating hyperspectral data using a spectrometer on-board the field robot. In order to achieve this, the robot must visit sampling locations that jointly improve a model of the environment while satisfying sampling constraints. However, current planners follow sub-optimal greedy strategies that are not scalable to larger regions. We demonstrate how the problem can be effectively defined in an MDP framework and propose a planning algorithm based on Monte Carlo Tree Search, which is devoid of the common drawbacks of existing planners and also provides superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.

An autonomous rover named Zoë, designed and built by Carnegie Mellon University's Robotics Institute, drilled into the soil of Chile's Atacama Desert in 2013 and discovered unusual, highly specialized microbes. The NASA-funded mission demonstrated how robots might someday find life on Mars. The astrobiology mission was led by the Robotics Institute and the SETI Institute to test technologies for searching for life underground. The microbial analyses of the soil samples recovered by Zoë were published Feb. 28 in the journal Frontiers of Microbiology. Zoë was equipped with a one-meter drill that recovered samples several times each day.

This, coupled with limited bandwidth and latencies, motivates on-board autonomy that ensures the quality of the science data return. Increasing quality of the data requires better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long-distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales.

Out on the rocky horizon, the robot has stopped dead in its tracks. "Uh, Dave, I got a big problem out here," a voice crackles over the radio. "OK," David Wettergreen replies carefully, peering off in the direction of the machine. "Big" turns out to be a new part for the robot that doesn't quite fit and so prevents the robot's cameras--its eyes--from turning properly. Back at the laboratory, this would be a quick fix, but the robot, Wettergreen, three geologists, two software engineers, two sociologists, an electrical engineer, a mechanical engineer, and a biologist are all out in the middle of Chile's vast Atacama Desert, [see map] many hours' drive from civilization. As he strides off to investigate, you get the sense Wettergreen's enjoying himself. For the better part of an hour, he and two colleagues will wrestle with the aberrant part [see photo, " All in a Day's Work"].

Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on high- dimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.

As planetary rovers expand their capabilities, traveling longer distances, deploying complex tools, and collecting voluminous scientific data, the requirements for intelligent guidance and control also grow. This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales. Specific capabilities include instrument calibration, visual targeting of selected features, an onboard database of collected data, and a long range path planner that guides the robot using analysis of current surface and prior satellite data. Field experiments in the Atacama Desert of Chile over the past decade demonstrate these capabilities and illustrate current challenges and future directions.